metadata
license: mit
datasets:
- mteb/banking77
language:
- en
pipeline_tag: text-classification
library_name: sentence-transformers
tags:
- mteb
- text
- transformers
- text-embeddings-inference
- sparse-encoder
- sparse
- csr
model-index:
- name: CSR
results:
- dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
metrics:
- type: accuracy
value: 0.899545
- type: f1
value: 0.899018
- type: f1_weighted
value: 0.899018
- type: main_score
value: 0.899545
task:
type: Classification
base_model:
- nvidia/NV-Embed-v2
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our Github.
Usage
📌 Tip: For NV-Embed-V2, using Transformers versions later than 4.47.0 may lead to performance degradation, as model_type=bidir_mistral in config.json is no longer supported.
We recommend using Transformers 4.47.0.
Sentence Transformers Usage
You can evaluate this model loaded by Sentence Transformers with the following code snippet:
import mteb
from sentence_transformers import SparseEncoder
model = SparseEncoder(
"Y-Research-Group/CSR-NV_Embed_v2-Classification-Banking77",
trust_remote_code=True
)
model.prompts = {
"Banking77Classification": "Instruct: Given a online banking query, find the corresponding intents\nQuery:"
}
task = mteb.get_tasks(tasks=["Banking77Classification"])
evaluation = mteb.MTEB(tasks=task)
evaluation.run(
model,
eval_splits=["test"],
output_folder="./results/Banking77Classification",
show_progress_bar=True
encode_kwargs={"convert_to_sparse_tensor": False, "batch_size": 8}
) # MTEB don't support sparse tensors yet, so we need to convert to dense tensors
Citation
@inproceedings{wenbeyond,
title={Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation},
author={Wen, Tiansheng and Wang, Yifei and Zeng, Zequn and Peng, Zhong and Su, Yudi and Liu, Xinyang and Chen, Bo and Liu, Hongwei and Jegelka, Stefanie and You, Chenyu},
booktitle={Forty-second International Conference on Machine Learning}
}